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Home/Questions/Spark/Big Data/How would you manage transitions to Glacier Instant Retrieval and Deep Archive?

How would you manage transitions to Glacier Instant Retrieval and Deep Archive?

Spark/Big Datahard0.6 min readPremium

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Glacier transitions: (1) Lifecycle rules—create rule to transition to Glacier Instant Retrieval or Deep Archive after X days. (2) Instant...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Capco
Key Concepts Tested
optimizationpartition

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
110 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Glacier transitions: (1) Lifecycle rules—create rule to transition to Glacier Instant Retrieval or Deep Archive after X days. (2) Instant Retrieval—ms retrieval, higher cost. (3) Deep Archive—hours retrieval, lowest cost. Configure: S3 Lifecycle > Add rule > Transition to Glacier. Use case: Move cold data (e.g., >90 days) to Deep Archive. Best practice: Tier based on access patterns; use Intelligent-Tiering for unknown; test retrieval before bulk transition; document retrieval SLAs.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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